Development of hybrid evolutionary algorithms for production scheduling of hot strip mill

Authors:
Yu-Wang Chen;Yong-Zai Lu;Ming Ge;Gen-Ke Yang;Chang-Chun Pan
Affiliations:
Department of Automation, Shanghai Jiaotong University, Shanghai 200240, PR China and Decision and Cognitive Sciences Research Centre, MBS, The University of Manchester, Manchester M15 6PB, UK;Department of Automation, Shanghai Jiaotong University, Shanghai 200240, PR China;Honeywell Technology & Solutions Lab, Shanghai 201203, PR China;Department of Automation, Shanghai Jiaotong University, Shanghai 200240, PR China;Department of Automation, Shanghai Jiaotong University, Shanghai 200240, PR China
Venue:
Computers and Operations Research
Year:
2012

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Abstract

A hot strip mill (HSM) produces hot rolled products from steel slabs, and is one of the most important production lines in a steel plant. The aim of HSM scheduling is to construct a rolling sequence that optimizes a set of given criteria under constraints. Due to the complexity in modeling the production process and optimizing the rolling sequence, the HSM scheduling is a challenging task for hot rolling production schedulers. This paper first introduces the HSM production process and requirements, and then reviews previous research on the modeling and optimization of the HSM scheduling problem. According to the practical requirements of hot rolling production, a mathematical model is formulated to describe two important scheduling sub-tasks: (1) selecting a subset of manufacturing orders and (2) generating an optimal rolling sequence from the selected manufacturing orders. Further, hybrid evolutionary algorithms with integration of genetic algorithm (GA) and extremal optimization (EO) are proposed to solve the HSM scheduling problem. Computational results on industrial data show that the proposed HSM scheduling solution can be applied in practice to provide satisfactory performance.